Distributed Localization for Wireless Distributed Networks in Indoor Environments

Distributed Localization for Wireless DistributedNetworks in Indoor Environments

Hermie P. Mendoza

Wireless @ VTVirginia Polytechnic and State University

June 28, 2011

Masters Thesis Defense Presentation

Agenda

1 Preliminaries of PL and WDC

2 Fingerprint-based PL

3 WDC-based Fingerprinting System

4 Algorithm Performance and Results

5 PL Demo

6 Conclusion and Future Work

Hermie P. Mendoza (VA Tech) Distributed Localization for WDN June 28, 2011 2 / 46

Preliminaries

Preliminaries Overview

Location-Awareness in Ubiquitous Computing

Position Location Fundamentals

Wireless Distributed Computing (WDC) Fundamentals

Why Position Location and WDC?


Preliminaries Position Location

Location Awareness in Ubiquitous Computing

Figure: User accessing location-based service on a smartphone.



The Principles of Positioning I

Positioning Problem: Reasonably localize an object within aglobal or local frame of reference.



The Principles of Positioning II

Figure: Summary of Position Location



The Principles of Positioning III

Figure: Summary of Position Location


Preliminaries Wireless Distributed Computing

What is WDC?

New paradigm emphasing distributed information services!

Figure: Information service shift from centralized to de-centralized computation.


Preliminaries Benefits

Benefits of WDC

Potential Benefits Results

1 Lower energy and powerconsumption per node

2 Efficient load balancingacross collaborating nodes

3 Harnesses availablenetwork resources

4 Robust, secure, & faulttolerant execution

5 Simplifies radio’s formfactor

Extends total networklifetime

Better resource demandand supply matching

Meets computationallatency requirements ofcomplex processing tasks

Attain stringent QoSrequirements

Economic cost savings



Benefits of WDC














Benefits of WDC














Benefits of WDC














Benefits of WDC













Preliminaries Location Awareness for WDC Paradigms

Location Awareness for WDC Paradigm

Improve overall wirelesscommunication system

Needed to achieveinteroperability

Figure: Cognitive radio sensingenvironment


Preliminaries PL and WDC

Motivations I

Localization is generally accomplished in a centralized manner at theexpense of a single network node’s resources. Can the problem ofpositioning be solved in a distributed manner or parallelized?

Figure: Resource constrained mobile phone.


Preliminaries PL and WDC

Motivations II

(a) Point inside the mall (b) Point inside an airport


Preliminaries Min Makespan

Min Makespan Problem I

Goal

Minimize the time taken to compute the individual localizationcalculations.

Problem Formulation

Given a set of J of m jobs and a set of N of n nodes, theprocessing time for a job j ∈ J on node i ∈ N is pij ∈ Z

+. Thenwe must find an assignment of the jobs J to the nodes N suchthat the makespan, or the completion time, is minimized.


Preliminaries Min Makespan

Min Makespan Problem II

Integer programming formulation

minimize t

subject to∑i∈N

xij = 1, j ∈ J

∑j∈J

xijpij ≤ t, i ∈ N

xij ∈ {0, 1} , i ∈ N, j ∈ J

(1)


Fingerprinting High Level Overview

Fingerprint Overview

Problem Formulation

The Fingerprint

Fingerprinting Algorithms


Fingerprinting Problem Formulation

Fingerprint Problem Statement

Problem Statement

Using only RSS observations of an arbitrary transmitter, locate andestimate its position in a distributed manner.

Goal

Distributed algorithms must be flexible and applicable for variousfingerprint-based positioning systems.

Computational nodes must form a WDCN.


Fingerprinting The Fingerprint

The Fingerprint I

Figure: Fingerprinting Concept


Fingerprinting The Fingerprint

The Fingerprint II

Mathematical Interpretation

(xi , yi ) = [FP1,FP2, . . . ,FPn] (2)

for fingerprint location i , using n sensor nodes.

Alternative Interpretation

f = (xi , yi ) = [FP1,FP2, . . . ,FPn] (3)

for fingerprint location i , using n sensor nodes.


Fingerprinting Fingerprinting Algorithms


Euclideandistance

Bayesianmodeling

NeuralNetworks

Deterministic positioning method

L2 =

√√√√ n∑i=1

∣∣FPi − FP′i

∣∣2 (4)

(x , y) = minFPi

√√√√ n∑i=1


∣∣2 (5)




Euclideandistance

Bayesianmodeling

NeuralNetworks

Probabilistic positioning method

P( l | f ) = P ( f | l)P(l)P(f )

, P(f ) �= 0 (4)

P( f | l) =n∏

j=1

P( fj | l) (5)

P ( lt | lt−1) =

∫P(lt | l ′t−1

)P(l

′t−1) dl

′t−1 (6)

(x , y) = maxl

P ( f | l)P(l) (7)




Euclideandistance

Bayesianmodeling

NeuralNetworks

Pattern Recognition



Distributed Target Localization I

Distributed Localization Approaches

Transfering computationally complex operations to a single node withgreater capabilities.

Parallelizing the position location calculations.



Distributed Target Localization II

Figure: Partitioning a service area for a WDCN.



Notations

f number of fingerprint locations

p number of partitions

(x , y) estimated position of user

gi vector of probabilities calculated by node i

FPi tuple of RSS at fingerprint location i

FP i vector of distances calculated by node i

FPi RSS received at sensor i

FP′i RSS database entry of sensor i

pi AOR or partition assigned to a node i



Distributed Euclidean Distance Algorithm (DEDA) I

Centralized Approach

(x , y) = minFPi

√√√√ f∑i=1


∣∣2 (4)



Distributed Euclidean Distance Algorithm (DEDA) II

Distributed Approach

Initialize FPi = 0.while pi is assigned and received, do

for all FP′j ∈ fj , do

FP i ←√∑j

k=1

∣∣FPk − FP′k

∣∣2end for

end while(xj , yj )← minFPi

∈ fj .return (xj , yj)



Distributed Bayesian Model Algorithm (DBMA) I

Centralized ApproachSEE

P( l |FPi ) =P (FPi | l)P(l)

P(FPi), P(FPi ) �= 0 (5)

ACT

P ( lt | lt−1) =

∫P(lt | l ′t−1

)P(l

′t−1) dl

′t−1 (6)

where lt is the current location and lt−1 is the previous location.



Distributed Bayesian Model Algorithm (DBMA) II

Distributed Approach

Initialize gi = 0.while pi is assigned and received, do

for all FP′j ∈ fj , do

gi(j)← P( j | {FP1,FP2, . . . ,FPn})end for

end whilereturn gi



Distributed Neural Networks (DNN) I

Types of Neural Networks

Multilayer Perceptron

Generalized Regression

Both will require a supervised learning to train the network.

Figure: Artificial Neural Network (ANN) Architecture



Distributed Neural Networks (DNN) II

Figure: WDCN with neural networks


WDC-based Fingerprinting System Overview

System Overview

Experimental Setup

Hardware and Software

The Radio Map


WDC-based Fingerprinting System Experimental Setup

Experimental Setup

Figure: System block diagram


WDC-based Fingerprinting System Hardware and Software

Hardware

Figure: USRP2 with custom WBX daughterboard


WDC-based Fingerprinting System Hardware and Software

Software

WDCN communications - GNU Radio

Fingerprint position processing - Python

Web-based user interface - PHP


ICTAS

ORIGIN

WDC-based Fingerprinting System The Radio Map

The Radio Map

0

Radio Map for 1st Floor ICTAS

10

-5

-15

-10

N22

N21

N20

-20

RSS

(dB)

N19

N18

N17

N16

-30

-25 N15

N14

N13

N12

-35

N11

-40

0 5 10 15 20 25 30 35 40 45

Position Number

Figure: Radio Map with 45 Positions


Algorithm Performance and Results

Algorithm Performance and Results


Algorithm Performance and Results Algorithm Evaluation

Algorithm Evaluation I

Comparison of Distributed Localization Algorithms

0 20 40 60 80 100 120 140 160 180 200 2203

4

5

X−direction (ft.)

Y−

dire

ctio

n (f

t.)

Distributed Neural Network − MLP

Actual PathEstimated Path

0 20 40 60 80 100 120 140 160 180 200 2203

4

5

X−direction (ft.)

Y−

dire

ctio

n (f

t.)

Distributed Neural Network − GR


0 20 40 60 80 100 120 140 160 180 200 2203

4

5

X−direction (ft.)

Y−

dire

ctio

n (f

t.)

Distributed Markov


0 20 40 60 80 100 120 140 160 180 200 2203

4

5

X−direction (ft.)

Y−

dire

ctio

n (f

t.)Distributed Euclidean


Figure: Comparison of solutions of distributed localization algorithms


Algorithm Performance and Results Algorithm Evaluation

Algorithm Evaluation II

50 100 150 20010

20

30

40

50

60

70

80

90

100

Error Radius (ft)

Per

cent

age

(%)

DEDADBMAGRNNMLPNN

Figure: Performance comparision of distributed localization algorithms


Algorithm Performance and Results Error Statistics

Error Statistics

Error statistics of distributed localization algorithmsAlgorithm Minimum Error Mean Error Max Error

DEDA 0 ft. 10.81 ft. 55 ft.

DBMA 0 ft. 35.33 ft. 220 ft.

GRNN 0 ft. 14.95 ft. 95 ft.

MLP 0 ft. 16.90 ft. 155 ft.

Average 0 ft. 19.50 ft. 131.25 ft.


Position Location Demo Overview

Overview

Functional Workflow of WDC process

Video of Demo


Position Location Demo Functional Workflow

Task dissemination and retrieval I

Figure: Phase I: Task dissemination


Position Location Demo Functional Workflow

Task dissemination and retrieval II

Figure: Phase II: Task retrieval


Position Location Demo Demo

Fingerprinting Position System


Position Location Demo Computational Complexity

Computational Complexity of Online Phase

Single node

Algorithm Computation Searching Sorting

EDA O(n) N/A O(n log n)BMA O(n) O (n (log u + 1)) O(n log n)

WDC slave node

Algorithm Computation Searching Sorting

DEDA O(n/4) N/A O(n/4 log n/4)DBMA O(n/4) O (n/4 (log u + 1)) O(n/4 log n/4)


Concluding Remarks Conclusions

Conclusions

Successful location estimates are highly dependent on quality anduniqueness of RF fingerprints.

Increasing spatial granularity of fingerprint positions does notnecessarily improve performance of position estimation.

Distributed PL is beneficial for large service areas with largedatabases.

De-centralized computations removes single-point of failure andsecurity intrusions.


Concluding Remarks Future Work

Future Work

Examine optimization techinque of multisplitting for conventional PLtechniques.

Expand distributed sensor system to all CORNET nodes and createmobile WDCN.

Implement demo with new UHD driver for USRP2.

Implement neural network for WDCN.


Concluding Remarks Future Work

Questions


Technology

Distributed Localization for Wireless Distributed Networks in Indoor Environments